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1. Predicting the Behavior of Sparsely-Sampled Systems Across Neurobiology and Epidemiology

   Armstrong, E (September 2023, Bulletin of mathematical biology)

   Inference is a term that encompasses many techniques including statistical data assimilation (SDA). Unlike machine learning, which is designed to harness predictive power from extremely large data sets, SDA is designed for sparsely-sampled systems. This is the realm of study of nonlinear dynamical systems in nature. Formulated as an optimization procedure, SDA can be considered a path-integral approach to state and parameter estimation. Within this formulation, we can use the physical principle of least action to identify optimal solutions: solutions that are consistent with both measurements and a dynamical model assumed to give rise to those measurements. I review examples from neurobiology and an epidemiological model tailored to the coronavirus SARS-CoV-2, to demonstrate the versatility of SDA across the sciences, and how these distinct applications possess commonalities that can inform one another. 

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2. FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model for Dynamical Simulation

   https://doi.org/10.1002/adts.202500062  

   Song, Zezheng; Yuan, Jiaxin; Yang, Haizhao (April 2025, Advanced Theory and Simulations)

   Abstract The fast simulation of dynamical systems is a key challenge in many scientific and engineering applications, such as weather forecasting, disease control, and drug discovery. With the recent success of deep learning, there is increasing interest in using neural networks to solve differential equations in a data‐driven manner. However, existing methods are either limited to specific types of differential equations or require large amounts of data for training. This restricts their practicality in many real‐world applications, where data is often scarce or expensive to obtain. To address this, a novel multi‐modal foundation model, namedFMint(FoundationModel based onInitialization) is proposed, to bridge the gap between human‐designed and data‐driven models for the fast simulation of dynamical systems. Built on a decoder‐only transformer architecture with in‐context learning, FMint utilizes both numerical and textual data to learn a universal error correction scheme for dynamical systems, using prompted sequences of coarse solutions from traditional solvers. The model is pre‐trained on a corpus of 400K ordinary differential equations (ODEs), and extensive experiments are performed on challenging ODEs that exhibit chaotic behavior and of high dimensionality. The results demonstrate the effectiveness of the proposed model in terms of both accuracy and efficiency compared to classical numerical solvers, highlighting FMint's potential as a general‐purpose solver for dynamical systems. This approach achieves an accuracy improvement of 1 to 2 orders of magnitude over state‐of‐the‐art dynamical system simulators, and delivers a 5X speedup compared to traditional numerical algorithms. The code for FMint is available athttps://github.com/margotyjx/FMint. 

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3. Optical Signatures of the Outer Radiation Belt Boundary

   https://doi.org/10.1029/2019GL083908  

   Sivadas, Nithin; Semeter, Joshua; Nishimura, Yukitoshi; Mrak, Sebastijan (August 2019, Geophysical Research Letters)

   Abstract We present observations that show structured diffuse aurora (SDA) correlated with electron precipitation directly from the outer boundary of the outer radiation belt. The SDA maps to the nightside transition region (∼9–12R_E) in the magnetic‐equatorial plane during a substorm growth phase. The energy flux of 100‐ to 300‐keV electrons lost from the outer boundary of the radiation belt is ∼0.4 mW/m², which is comparable to electron dropouts >100 keV during magnetic storms. The latitudinal dispersion of energetic electrons observed in the ionosphere with energetic electrons more equatorward suggests nonadiabatic scattering from a thinning current sheet. The GLobal airglOW (GLOW) model shows significant optical contributions (up to 46%) from electrons >30 keV within the SDA. Ground‐ and space‐based measurements are consistent with the conclusion that the SDA marks the outer radiation belt boundary during substorm growth phase. 

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4. Building capacity in engineering education research through collaborative secondary data analysis

   https://doi.org/10.1080/22054952.2023.2214462  

   Paretti, Marie C.; Case, Jennifer M.; Benson, Lisa; Delaine, David A.; Jordan, Shawn; Kajfez, Rachel L.; Lord, Susan M.; Matusovich, Holly M.; Young, E. Tyler; Zastavker, Yevgeniya V. (January 2023, Australasian Journal of Engineering Education)

   This paper proposes the use of collaborative secondary data analysis (SDA) as a tool for building capacity in engineering education research. We first characterise the value of collaborative SDA as a tool to help emerging researchers develop skills in qualitative data analysis. We then describe an ongoing collaboration that involves a series of workshops as well as two pilot projects that seek to develop and test frameworks and practices for SDA in engineering education research. We identify emerging benefits and practical challenges associated with implementing SDA as a capacity building tool, and conclude with a discussion of future work. 

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5. Video-Based Social Distancing: Evaluation in the COSMOS Testbed

   https://doi.org/10.1109/JIOT.2023.3305587  

   Ghasemi, Mahshid; Yang, Zhengye; Sun, Mingfei; Ye, Hongzhe; Xiong, Zihao; Ghaderi, Javad; Kostic, Zoran; Zussman, Gil (January 2023, IEEE Internet of Things Journal)

   Social distancing is an effective public health tool to reduce the spread of respiratory pandemics such as COVID-19. To analyze compliance with social distancing policies, we design two video-based pipelines for social distancing analysis, namely, Auto-SDA and B-SDA. Auto-SDA (Automated video-based Social Distancing Analyzer) is designed to measure social distancing using street-level cameras. To avoid privacy concerns of using street-level cameras, we further develop B-SDA (Bird’s eye view Social Distancing Analyzer), which uses bird’s eye view cameras, thereby preserving pedestrian’s privacy. We used the COSMOS testbed deployed in West Harlem, New York City, to evaluate both pipelines. In particular, Auto-SDA and B-SDA are applied on videos recorded by two of COSMOS cameras deployed on the 2nd floor (street-level) and 12th floor (bird’s eye view) of Columbia University’s Mudd building, looking at 120th St. and Amsterdam Ave. intersection, New York City. Videos are recorded before and during the peak of the pandemic, as well as after the vaccines became broadly available. The results represent the impact of social distancing policies on pedestrians’ social behavior. For example, the analysis shows that after the lockdown, less than 55% of the pedestrians failed to adhere to the social distancing policies, whereas this percentage increased to 65% after the vaccines’ availability. Moreover, after the lockdown, 0-20% of the pedestrians were affiliated with a social group, compared to 10-45% once the vaccines became available. The results also show that the percentage of face-to-face failures has decreased from 42.3% (pre-pandemic) to 20.7%(after the lockdown). 

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